Data overview

## # A tibble: 406 × 36
##      age gender education_level employment_status         other_economic_activ…¹
##    <dbl> <fct>  <fct>           <fct>                     <fct>                 
##  1    23 Male   tertiary        student                   <NA>                  
##  2    33 Male   tertiary        farmer/agriculturist      barbering             
##  3    23 Female tertiary        student                   <NA>                  
##  4    37 Male   tertiary        civil servant/public sec… farming               
##  5    42 Male   tertiary        civil servant/public sec… business              
##  6    37 Male   tertiary        civil servant/public sec… farming               
##  7    41 Male   tertiary        civil servant/public sec… farming               
##  8    52 Male   tertiary        self-employed/entreprene… consultancy           
##  9    41 Male   primary         civil servant/public sec… farming               
## 10    38 Female tertiary        civil servant/public sec… <NA>                  
## # ℹ 396 more rows
## # ℹ abbreviated name: ¹​other_economic_activity
## # ℹ 31 more variables: crops_grown <fct>, length_residence <fct>,
## #   ever_used_weed <fct>, age_first_use <fct>, smoking_frequency <fct>,
## #   weed_trend_change <fct>, consumption_method <fct>, weed_source <fct>,
## #   reason_for_use <fct>, social_group_influence <fct>,
## #   availability_role <fct>, health_challenges <fct>, …
  • Observations

The dataset contains 406 observations across 36 variables, capturing demographic details, weed smoking behaviors, causes, outcomes, and community perspectives in Anfoega, North Dayi District.

Demographic Profile

  • Observations

Age distribution

Age: Respondents range from 18 to 69 years (mean: 40.2, median: 39). Age groups: 18–25 (40), 26–35 (113), 36–45 (122), 46–55 (82), 56+ (49).

Gender: Male-dominated (243 vs. 163 female).

Education: Predominantly tertiary-educated (244), followed by primary (81) and SHS (81).

Employment: The majority are employed in the public sector (228, 56.2%), followed by agriculture (70, 17.2%), private/business (49, 12.1%), students (27, 6.7%), retired (23, 5.7%), and transport (9, 2.2%).

Residence: Most respondents are long-term residents: Very Long (>10 years: 159, 39.2%), Medium (4–6 years: 116, 28.6%), Long (7–10 years: 66, 16.3%), and New (<3 years: 65, 16.0%).

Objective 1

To Examine the trends in weed smoking in Anfoega.

Perceived Trend in Weed Smoking in Anfoega
Trend Direction Count Percentage (%)
increased significantly 130 82.8
increased slightly 27 17.2
Age at First Weed Use Among Users
Age Category Count Percentage (%)
15–19 years 29 18.5
20 years and above 128 81.5
Community Perception of Weed Smoking Prevalence
Prevalence Rating Count Percentage (%)
1 19 7.6
2 40 16.1
3 45 18.1
4 74 29.7
5 71 28.5
Frequency of Weed Smoking Among Users
Smoking Frequency Count Percentage (%)
daily 22 14.0
occasiolly 37 23.6
rarely 98 62.4
Trend Factors
trend_factors n percentage
Increased Availability Of Weed 94 37.8
Peer Pressure 47 18.9
Increased Availability Of Weed Peer Pressure Curiosity 18 7.2
Curiosity 11 4.4
Increased Availability Of Weed Influence From Social Media Peer Pressure Changing Societal Attitudes Towards Weed Social Acceptance Curiosity 10 4.0
Economic Hardship/Stress Relief 9 3.6
Increased Availability Of Weed Changing Societal Attitudes Towards Weed Economic Hardship/Stress Relief 9 3.6
Increased Availability Of Weed Influence From Social Media Peer Pressure Changing Societal Attitudes Towards Weed Economic Hardship/Stress Relief Curiosity 9 3.6
Increased Availability Of Weed Changing Societal Attitudes Towards Weed Social Acceptance 8 3.2
Influence From Social Media Peer Pressure Curiosity 8 3.2 
## Key Factors Contributing to Weed Smoking Trends

## Statistical Tests
## Chi-Square Test for Weed Trend Change by Gender:
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  table(weed_df$gender, weed_df$weed_trend_change)
## X-squared = 7.811, df = 1, p-value = 0.005193
## 
## Chi-Square Test for Weed Trend Change by Education Level:
## 
##  Pearson's Chi-squared test
## 
## data:  table(weed_df$education_level, weed_df$weed_trend_change)
## X-squared = 1.6554, df = 2, p-value = 0.437
## 
## Chi-Square Test for Weed Trend Change by Age Group:
## 
##  Pearson's Chi-squared test
## 
## data:  table(weed_df$age_group, weed_df$weed_trend_change)
## X-squared = 8.2439, df = 4, p-value = 0.08304
## 
## Chi-Square Test for Weed Trend Change by Length of Residence:
## 
##  Pearson's Chi-squared test
## 
## data:  table(weed_df$length_residence, weed_df$weed_trend_change)
## X-squared = 18.144, df = 3, p-value = 0.0004108
## Chi-Square Test for Weed Use by Gender:
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  table(weed_df$gender, weed_df$ever_used_weed)
## X-squared = 0.0094654, df = 1, p-value = 0.9225
## 
## Chi-Square Test for Weed Use by Education Level:
## 
##  Pearson's Chi-squared test
## 
## data:  table(weed_df$education_level, weed_df$ever_used_weed)
## X-squared = 13.981, df = 2, p-value = 0.0009206
## 
## Chi-Square Test for Weed Use by Age Group:
## 
##  Pearson's Chi-squared test
## 
## data:  table(weed_df$age_group, weed_df$ever_used_weed)
## X-squared = 8.0997, df = 4, p-value = 0.088
## 
## Chi-Square Test for Weed Use by Length of Residence:
## 
##  Pearson's Chi-squared test
## 
## data:  table(weed_df$length_residence, weed_df$ever_used_weed)
## X-squared = 41.121, df = 3, p-value = 6.163e-09
  • Observations for Objective 1

Objective 1 aimed to examine trends in weed smoking in Anfoega, North Dayi District.

Weed Smoking Prevalence

Finding: 38.7% (157) of respondents have ever used weed , while 61.3% (249) have not.

Observation: The substantial prevalence indicates weed smoking is a notable issue in Anfoega, with nearly two in five respondents reporting use.

Perceived Trends

Finding: Among the 157 respondents reporting weed use, 82.8% (130) perceive a significant increase in weed smoking over the past five years, and 17.2% (27) note a slight increase.

Observation: The overwhelming perception of a significant upward trend suggests a growing community concern about escalating weed use.

Age at First Use

Finding: Among users (157), 81.5% (128) first used weed at 20 years or older, while 18.5% (29) started at 15–19 years.

Observation: Late initiation (20+ years) is predominant, but teenage use (15–19 years) highlights a vulnerable youth segment.

Perceived Youth Starting Age

Finding: Community perception indicates youth typically start weed smoking at 15–19 years (101, 40.6%), followed by 10–14 years (56, 22.5%), 20+ years (62, 24.9%), and below 10 years (19, 7.6%).

Observation: The focus on 15–19 years aligns with user reports, emphasizing adolescence as a critical period for intervention.

Prevalence Rating

Finding: On a 1–5 scale (1 = very low, 5 = very high), prevalence is rated as 4 (29.7%, 74) or 5 (28.5%, 71), with a median of 4. Lower ratings (1: 7.6%, 19; 2: 16.1%, 40; 3: 18.1%, 45) are less common.

Observation: High ratings (58.2% at 4 or 5) reinforce the perception of widespread weed use, particularly among youth.

Influential Groups

Finding: Friends (217, 58.0%) are the primary group perceived to promote weed use, followed by friends and social media combined (32, 8.6%).

Observation: Peer influence dominates, underscoring the social nature of weed smoking trends in Anfoega.

Smoking Frequency

Finding: Among users, 62.4% (98) smoke weed rarely, 23.6% (37) occasionally, and 14.0% (22) daily.

Observation: Predominantly rare use suggests weed smoking is not habitual for most users, but daily users indicate a subset with potential dependency.

Trend Factors

Finding: Key factors driving trends include increased availability (top factor), peer pressure, and curiosity. The word cloud highlights these terms prominently among the top 10 factors.

Observation: Availability and social influences are critical drivers, suggesting external supply and peer dynamics fuel the upward trend.

Demographic Associations (Chi-Square Tests)

Weed Use:

Gender: No significant association (p = 0.9), indicating similar prevalence across males (59.9%) and females (40.1%).

Education: Significant association (p = 0.0009), with tertiary-educated (60.1%) users more likely to report use, possibly due to reporting bias or social acceptance.

Age Group: Marginal association (p = 0.09), with trends suggesting younger groups (18–25, 9.9%; 26–35, 27.8%) may differ from older groups (36–45, 30.0%).

Residence: Strong association (p < 0.0001), with longer residents (>10 years, 39.2%) more likely to report use, reflecting deeper community exposure.

Trend Perception:

Gender: Significant association (p = 0.005), with males more likely to perceive significant increases, possibly due to greater social exposure.

Education: No association (p = 0.4), indicating uniform trend perceptions across education levels.

Age Group: Marginal association (p = 0.08), with younger groups potentially perceiving stronger increases.

Residence: Strong association (p = 0.0004), with longer residents perceiving greater increases, likely due to historical context.

  • Implications for Objective 1 Significant Trend: The high prevalence (38.7%) and perceived significant increase (82.8%) confirm weed smoking as a growing issue in Anfoega.

Youth Vulnerability: Initiation at 15–19 years (18.5% of users, 40.6% perceived) highlights adolescence as a key intervention period.

Social Drivers: Friends and availability drive trends, necessitating peer-focused and supply-side interventions.

Demographic Variations: Longer residents and tertiary-educated individuals shape perceptions and use patterns, suggesting targeted community engagement.

Objective 2

Identify the major causes of weed smoking in Anfoega

Reasons for Initiating Weed Smoking in Anfoega
Reason Count Percentage (%)
curiosity 71 45.2
peer pressure 36 22.9
stress relief 50 31.8
Groups Influencing Youth Weed Smoking in Anfoega
Influential Group Count Percentage (%)
friends 217 87.1
friends social media 32 12.9
Top 10 Factors Contributing to Weed Smoking Trends
Contributing Factor Frequency Percentage (%)
increased availability of weed 94 37.8
peer pressure 47 18.9
increased availability of weed peer pressure curiosity 18 7.2
curiosity 11 4.4
increased availability of weed influence from social media peer pressure changing societal attitudes towards weed social acceptance curiosity 10 4.0
economic hardship/stress relief 9 3.6
increased availability of weed changing societal attitudes towards weed economic hardship/stress relief 9 3.6
increased availability of weed influence from social media peer pressure changing societal attitudes towards weed economic hardship/stress relief curiosity 9 3.6
increased availability of weed changing societal attitudes towards weed social acceptance 8 3.2
influence from social media peer pressure curiosity 8 3.2
Common sources of weed
weed_source n percentage
Friends 91 58.0
Farms 41 26.1
Local Markets 25 15.9 
## 
## 
##  AVAILABILITY AND ACCESS FACTORS
## ===================================
Perceived Role of Availability in Weed Use
Role of Availability Count Percentage (%)
significant role 124 79
no role 33 21

  • Stattistical Tests
## Chi-Square Test for Reason for Use by Gender:
## 
##  Pearson's Chi-squared test
## 
## data:  table(weed_df$gender, weed_df$reason_for_use)
## X-squared = 2.7253, df = 2, p-value = 0.256
## 
## Chi-Square Test for Reason for Use by Education Group:
## 
##  Pearson's Chi-squared test
## 
## data:  table(weed_df$education_group, weed_df$reason_for_use)
## X-squared = 5.8861, df = 4, p-value = 0.2078
## 
## Chi-Square Test for Reason for Use by Age Group:
## 
##  Pearson's Chi-squared test
## 
## data:  table(weed_df$age_group, weed_df$reason_for_use)
## X-squared = 46.652, df = 8, p-value = 1.786e-07
## 
## Chi-Square Test for Reason for Use by Employment Group:
## 
##  Pearson's Chi-squared test
## 
## data:  table(weed_df$employment_group, weed_df$reason_for_use)
## X-squared = 28.818, df = 10, p-value = 0.001334
## 
## Chi-Square Test for Reason for Use by Residence Group:
## 
##  Pearson's Chi-squared test
## 
## data:  table(weed_df$residence_group, weed_df$reason_for_use)
## X-squared = 38.743, df = 6, p-value = 8.038e-07
## Chi-Square Test for Influential Group by Gender:
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  table(weed_df$gender, weed_df$influential_group)
## X-squared = 9.0552, df = 1, p-value = 0.002619
## 
## Chi-Square Test for Influential Group by Education Group:
## 
##  Pearson's Chi-squared test
## 
## data:  table(weed_df$education_group, weed_df$influential_group)
## X-squared = 1.4139, df = 2, p-value = 0.4931
## 
## Chi-Square Test for Influential Group by Age Group:
## 
##  Pearson's Chi-squared test
## 
## data:  table(weed_df$age_group, weed_df$influential_group)
## X-squared = 7.794, df = 4, p-value = 0.09942
## 
## Call:
## glm(formula = ever_used_weed ~ employment_group, family = "binomial", 
##     data = weed_df)
## 
## Coefficients:
##                                  Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                       -0.3463     0.2426  -1.427 0.153541    
## employment_groupPrivate/Business  -2.0741     0.5754  -3.605 0.000313 ***
## employment_groupPublic Sector      0.3814     0.2764   1.380 0.167729    
## employment_groupRetired           -2.0051     0.7788  -2.575 0.010033 *  
## employment_groupStudents          -1.4029     0.5936  -2.363 0.018106 *  
## employment_groupTransport         -0.9065     0.8377  -1.082 0.279198    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 541.81  on 405  degrees of freedom
## Residual deviance: 484.46  on 400  degrees of freedom
## AIC: 496.46
## 
## Number of Fisher Scoring iterations: 5
  • Observations for Objective 2

Objective 2 aimed to identify the major causes of weed smoking in Anfoega, North Dayi District.

Primary Reasons for Weed Smoking

Finding: Among users (157), curiosity is the leading reason for initiating weed smoking (71, 45.2%), followed by stress relief (50, 31.8%) and peer pressure (36, 22.9%).

Observation: Curiosity and stress relief dominate, indicating personal motivations, while peer pressure highlights a social driver, particularly among younger users.

Social Group Influence

Finding: Among users, friends are the sole reported social influence (157, 100%). Community-wide, friends (217, 87.1%) and friends combined with social media (32, 12.9%) are perceived as key influencers of youth weed smoking.

Observation: Peer influence, especially from friends, is a critical social driver, amplified by social media in some cases, underscoring the role of social networks.

Contributing Trend Factors

Finding: Increased availability of weed (94, 37.8%) and peer pressure (47, 18.9%) are the top factors driving weed smoking trends, followed by combinations like availability, peer pressure, and curiosity (18, 7.2%) and curiosity alone (11, 4.4%).

Observation: Availability and peer pressure are primary environmental and social causes, with curiosity reinforcing personal motivations.

Weed Sources

Finding: Among users, friends are the primary source of weed (91, 58.0%), followed by farms (41, 26.1%) and local markets (25, 15.9%).

Observation: The reliance on friends as a source reinforces peer influence, while farms and markets indicate local accessibility, facilitating use.

Role of Availability

Finding: 79% (124) of respondents perceive availability as playing a significant role in weed use, while 21% (33) see no role.

Observation: High perceived availability is a major environmental driver, enabling easy access and contributing to rising trends.

Age and Initiation Patterns

Finding: Among users, 81.5% (128) started weed smoking at 20 years or older, and 18.5% (29) at 15–19 years. Community perception identifies 15–19 years (101, 42.4%) as the typical youth starting age, followed by 20+ years (62, 26.1%), 10–14 years (56, 23.5%), and below 10 years (19, 8.0%).

Observation: Teenage initiation (15–19 years) is a critical risk period, aligning with peer-driven influences, while later initiation (20+) is more common among users.

Demographic Risk Factors (usage_by_age, usage_by_gender, usage_by_education, Chi-Square Tests, Logistic Regression)

Age Group: Finding: Usage rates are highest among 46–55 years (47.6%, 39/82), 56+ years (44.9%, 22/49), and 18–25 years (42.5%, 17/40). Reasons for use vary significantly by age group (p = 0.0000002), with younger groups likely driven by curiosity/peer pressure and older groups by stress relief.

Observation: Both youth and older adults are at risk, with distinct motivations by age.

Gender: Finding: Usage rates are similar (females: 39.3%, 64/163; males: 38.3%, 93/243; p = 0.3 for reasons), but influential groups differ by gender (p = 0.003), with males more likely to cite friends.

Observation: Gender influences social drivers, not prevalence or reasons.

Education: Finding: Usage rates are highest among SHS (53.1%, 43/81) and primary (45.7%, 37/81) compared to tertiary (31.6%, 77/244). Reasons (p = 0.2) and influential groups (p = 0.5) do not vary significantly by education.

Observation: Lower education levels are associated with higher use, possibly due to socioeconomic factors, but motivations are consistent.

Employment: Finding: Reasons for use vary by employment group (p = 0.001). Logistic regression shows lower odds of use among private/business (p = 0.00031), retired (p = 0.01003), and students (p = 0.01811) compared to agriculture (reference).

Observation: Employment status influences motivations, with agricultural workers at higher risk, possibly due to stress or availability.

Residence: Finding: Reasons for use vary significantly by residence duration (p = 0.0000008), with longer residents (>10 years, 39.2%) more likely to cite stress relief or availability-driven use.

Observation: Long-term residency increases exposure to local weed culture and access.

Secondary Outcomes (Health, Social, Financial, Legal) Finding: Most users report no health (72%, 113), social (84.1%, 132), or financial (84.1%, 132) impacts, with 28% (44) noting reduced concentration/memory loss and 15.9% (25) reporting negative social/financial impacts. No legal consequences are reported (100%, 157).

Observation: Limited negative outcomes may reflect social acceptance or underreporting, but memory loss is a notable concern linked to use.

  • Implications for Objective 2

Major Causes: Curiosity, peer pressure, and stress relief are primary personal and social drivers, amplified by high availability and friend-based sourcing.

Social Networks: Friends dominate influence and supply, highlighting the need for peer-focused interventions.

Environmental Factors: Local farms and markets facilitate access, necessitating supply-side controls.

Demographic Risks: Youth (15–19 years), lower-educated (SHS/primary), agricultural workers, and long-term residents are high-risk groups, requiring targeted strategies.

Objective 3

Evaluate the negative effects of weed smoking in Anfoega

Perceived Health Challenges Among Weed Users
Health Impact Count Percentage (%)
none 113 72
reduced concentration and memory loss 44 28 
## [1] "Health impact by smoking frequency:"
## # A tibble: 3 × 4
##   smoking_frequency total_users health_problems health_impact_rate
##   <fct>                   <int>           <int>              <dbl>
## 1 occasiolly                 37              29               78.4
## 2 rarely                     98              15               15.3
## 3 daily                      22               0                0
Perceived Social Relationship Impact Among Weed Users
Social Impact Count Percentage (%)
No effect 132 84.1
Yes, negatively 25 15.9 
## 
## --- Table: Perceived Financial Impact Among Weed Users ---
Perceived Financial Impact Among Weed Users
Financial Impact Count Percentage (%)
No effect 132 84.1
Yes, negatively 25 15.9 
## 
## --- Table: Perceived Legal Consequences Among Weed Users ---
Perceived Legal Consequences Among Weed Users
Legal Consequences Count Percentage (%)
No legal consequences 157 100 
## 
##  Table: Perceived Social Stigma Associated with Weed Smoking ---
Perceived Social Stigma Associated with Weed Smoking
Stigma Level Count Percentage (%)
high stigma 89 36.3
very high stigma 47 19.2
low stigma 43 17.6
moderate stigma 38 15.5
no stigma 28 11.4 

## 
## 
##  DEMOGRAPHIC RISK FACTORS
## ============================
Usage rates by age group
age_group total users usage_rate
46-55 years 82 39 47.6
56+ years 49 22 44.9
18-25 years 40 17 42.5
36-45 years 122 46 37.7
26-35 years 113 33 29.2
Usage rates by gender
gender total users usage_rate
Female 163 64 39.3
Male 243 93 38.3
Usage rates by education level
education_level total users usage_rate
shs 81 43 53.1
primary 81 37 45.7
tertiary 244 77 31.6 
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  table_health_gender
## X-squared = 4.0479, df = 1, p-value = 0.04423
  • Observations for Objective 3

Objective 3 aimed to evaluate the negative effects of weed smoking in Anfoega, North Dayi District

Health Challenges

Finding: Among weed users (157), 72% (113) report no health challenges, while 28% (44) experience reduced concentration and memory loss.

Observation: The majority of users perceive no health impacts, but cognitive impairment is a significant concern for a notable minority, indicating potential long-term risks.

Social Relationship Impact

Finding: 84.1% (132) of users report no effect on social relationships, while 15.9% (25) note negative impacts.

Observation: Weed smoking has minimal social consequences for most users, suggesting social acceptance or low disruption, but negative effects for a small group may strain personal connections.

Financial Impact

Finding: 84.1% (132) of users report no financial impact, while 15.9% (25) experience negative financial consequences.

Observation: Financial burdens are rare, possibly due to low-cost access (e.g., friends, farms), but negative impacts for some indicate potential economic strain.

Legal Consequences

Finding: 100% (157) of users report no legal consequences.

Observation: The absence of legal repercussions suggests weed use is either discreet, socially tolerated, or not heavily policed in Anfoega, reducing legal risks.

Social Stigma

Finding: Community-wide, 36.3% (89) perceive high stigma, 19.2% (47) very high stigma, 17.6% (43) low stigma, 15.5% (38) moderate stigma, and 11.4% (28) no stigma associated with weed smoking.

Observation: Significant stigma (55.5% high or very high) persists, potentially deterring open use or reporting, despite minimal reported negative impacts among users.

Impact by Smoking Frequency

Finding: Health impact rates vary by smoking frequency: occasional users (78.4%, 29/37) report the highest rate of health problems, followed by rare users (15.3%, 15/98), while daily users (0%, 0/22) report no health issues.

Observation: The high impact rate among occasional users is unexpected and may reflect misreporting or specific usage patterns (e.g., higher doses). Daily users’ lack of reported issues could indicate tolerance or underreporting.

Gender Differences (Chi-Square Test)

Finding: Health challenges are significantly associated with gender (p = 0.04), with differences in reported impacts (e.g., males or females may report memory loss differently).

Observation: Gender influences the perception or reporting of health effects, warranting further exploration of specific differences.

  • Implications for Objective 3

Limited Negative Effects: Most users report no health, social, financial, or legal impacts, suggesting weed smoking is socially integrated or has low immediate consequences in Anfoega.

Health Risks: Cognitive impairment (28%) is a key concern, particularly for occasional users, highlighting the need for targeted health education.

Social Stigma: High stigma (55.5%) may suppress open use or accurate reporting, complicating intervention efforts.

Demographic Variations: Gender differences in health impacts suggest tailored approaches to address specific vulnerabilities.

Objective 4

Examine innovative strategies to reduce the prevalence of weed smoking in Anfoega

Proposed Strategies to Reduce Weed Smoking in Anfoega
Strategy Count Percentage (%)
awareness campaigns 149 37.1
awareness campaigns community rehabilitation centers 7 1.7
awareness campaigns economic empowerment programs 9 2.2
awareness campaigns economic empowerment programs community rehabilitation centers 9 2.2
awareness campaigns stricter law enforcement 27 6.7
awareness campaigns stricter law enforcement economic empowerment programs 5 1.2
awareness campaigns stricter law enforcement economic empowerment programs community rehabilitation centers 16 4.0
awareness campaigns stricter law enforcement others 8 2.0
community rehabilitation centers 4 1.0
economic empowerment programs 29 7.2
economic empowerment programs community rehabilitation centers 5 1.2
others 23 5.7
stricter law enforcement 102 25.4
stricter law enforcement economic empowerment programs community rehabilitation centers 9 2.2
Perceived Effectiveness of Interventions (1=Not Effective, 5=Very Effective)
Effectiveness Rating Count Percentage (%)
1 139 34.6
2 108 26.9
3 31 7.7
4 76 18.9
5 48 11.9 

## 
## --- Table: Preferred Lead Group for Interventions ---
Recommended Lead Groups for Combating Weed Smoking in Anfoega
Lead Group Count Percentage (%)
families 122 30.3
community leaders 68 16.9
religious leaders 47 11.7
community leaders individuals 25 6.2
government 25 6.2
government religious leaders community leaders families 25 6.2
government religious leaders community leaders ngos individuals families 19 4.7
government community leaders families 15 3.7
community leaders families 11 2.7
government religious leaders community leaders individuals families 10 2.5
government religious leaders community leaders ngos families 10 2.5
government community leaders individuals families 9 2.2
religious leaders community leaders families 7 1.7
ngos individuals families 5 1.2
individuals families 4 1.0
Methods to Discourage Youth from Weed Smoking in Anfoega
Discouragement Method Count Percentage (%)
peer mentorship 107 26.6
parental guidance peer mentorship media campaigns increased law enforcement 63 15.7
increased law enforcement 56 13.9
parental guidance peer mentorship media campaigns 49 12.2
parental guidance 48 11.9
parental guidance peer mentorship 27 6.7
parental guidance peer mentorship increased law enforcement 19 4.7
parental guidance media campaigns increased law enforcement 18 4.5
parental guidance increased law enforcement 10 2.5
peer mentorship media campaigns 5 1.2
Preferred discouragement methods
discouragement_method n percentage method_category
peer mentorship 107 26.6 Peer-Based
parental guidance peer mentorship media campaigns increased law enforcement 63 15.7 Peer-Based
increased law enforcement 56 13.9 Legal/Enforcement
parental guidance peer mentorship media campaigns 49 12.2 Peer-Based
parental guidance 48 11.9 Family-Based
parental guidance peer mentorship 27 6.7 Peer-Based
parental guidance peer mentorship increased law enforcement 19 4.7 Peer-Based
parental guidance media campaigns increased law enforcement 18 4.5 Family-Based
parental guidance increased law enforcement 10 2.5 Family-Based
peer mentorship media campaigns 5 1.2 Peer-Based
Discouragement method categories
method_category total_supporters methods_count
Peer-Based 270 6
Family-Based 76 3
Legal/Enforcement 56 1
Top 2 Specific Suggestions for Reducing Weed Smoking
Suggestion Frequency Percentage (%)
education 23 74.2
stop or limit the production 8 25.8
Reduction strategies proposed by community
reduction_strategies n percentage strategy_type
awareness campaigns 149 37.1 Education/Awareness
stricter law enforcement 102 25.4 Legal/Enforcement
economic empowerment programs 29 7.2 Economic
awareness campaigns stricter law enforcement 27 6.7 Education/Awareness
others 23 5.7 Mixed/Other
awareness campaigns stricter law enforcement economic empowerment programs community rehabilitation centers 16 4.0 Education/Awareness
awareness campaigns economic empowerment programs 9 2.2 Education/Awareness
awareness campaigns economic empowerment programs community rehabilitation centers 9 2.2 Education/Awareness
stricter law enforcement economic empowerment programs community rehabilitation centers 9 2.2 Legal/Enforcement
awareness campaigns stricter law enforcement others 8 2.0 Education/Awareness
awareness campaigns community rehabilitation centers 7 1.7 Education/Awareness
awareness campaigns stricter law enforcement economic empowerment programs 5 1.2 Education/Awareness
economic empowerment programs community rehabilitation centers 5 1.2 Economic
community rehabilitation centers 4 1.0 Community-Based
Strategy types by community support
strategy_type total_supporters strategies_count avg_support
Education/Awareness 230 8 28.8
Legal/Enforcement 111 2 55.5
Economic 34 2 17.0
Mixed/Other 23 1 23.0
Community-Based 4 1 4.0 
## ======================================
Community ratings of intervention effectiveness
intervention_effectiveness n percentage effectiveness_level
1 139 34.6 Low Effectiveness
2 108 26.9 Low Effectiveness
4 76 18.9 High Effectiveness
5 48 11.9 High Effectiveness
3 31 7.7 Moderate Effectiveness
Effectiveness level summary
effectiveness_level total_responses percentage
High Effectiveness 124 30.8
Low Effectiveness 247 61.5
Moderate Effectiveness 31 7.7
  • Observations for Objective 4

Objective 4 aimed to examine innovative strategies to reduce the prevalence of weed smoking in Anfoega, North Dayi District.

Proposed Reduction Strategies

Finding: Awareness campaigns (149, 37.1%) and stricter law enforcement (102, 25.4%) are the most supported strategies, followed by economic empowerment programs (29, 7.2%) and combinations like awareness campaigns with law enforcement (27, 6.7%).

Observation: Education/awareness (230 supporters, 57.2%) and legal/enforcement (111, 27.6%) dominate, reflecting a preference for preventive education and regulatory measures. Economic strategies (34, 8.5%) suggest innovative livelihood-focused approaches.

Perceived Intervention Effectiveness

Finding: 61.5% (247) rate interventions as low effectiveness (1: 34.6%, 139; 2: 26.9%, 108), 7.7% (31) as moderate (3), and 30.8% (124) as high (4: 18.9%, 76; 5: 11.9%, 48).

Observation: Pessimism about intervention success (61.5% low ratings) suggests a need for innovative, evidence-based strategies to build community trust and demonstrate impact.

Preferred Lead Groups

Finding: Families (122, 30.3%) are the most preferred lead group, followed by community leaders (68, 16.9%), religious leaders (47, 11.7%), and combinations like government with community leaders and families (25, 6.2%).

Observation: Community-based leadership (families, community/religious leaders) is strongly favored, emphasizing local trust and social structures over external entities like government or NGOs.

Discouragement Methods

Finding: Peer mentorship (107, 26.6%) and comprehensive approaches combining parental guidance, peer mentorship, media campaigns, and law enforcement (63, 15.7%) are top methods, followed by increased law enforcement (56, 13.9%) and parental guidance (48, 11.9%).

Observation: Peer-based methods (270 supporters, 67.2%) and family-based approaches (76, 18.9%) are highly supported, highlighting the role of social influence and family in deterring youth use.

Specific Suggestions

Finding: Education (23, 74.2%) and stopping or limiting weed production (8, 25.8%) are the top specific suggestions among the few provided (31 non-“none” responses).

Observation: Education aligns with awareness campaigns, while production limits suggest a supply-side innovation, though limited responses (375 NAs) restrict depth.

  • Implications for Objective 4

Community-Driven Strategies: Awareness campaigns, peer mentorship, and family-led efforts are strongly endorsed, leveraging local social structures to reduce prevalence.

Innovative Approaches: Economic empowerment and production limits offer novel solutions, addressing socioeconomic and supply-side drivers.

Challenges: Low perceived effectiveness (61.5%) suggests skepticism, necessitating pilot programs to demonstrate success and build trust.

Leadership: Families and community leaders are ideal facilitators, ensuring culturally relevant interventions.

Conclusion

The study investigated weed smoking in Anfoega, North Dayi District, through a survey of 406 respondents, analyzing trends, causes, negative effects, and innovative strategies to reduce prevalence. The dataset reveals a significant and growing issue with weed smoking, driven by social and environmental factors, with limited negative impacts but notable community concern and support for multi-faceted interventions.

Key findings across the four objectives

Objective 1: Trends in Weed Smoking

Key Finding: Weed smoking is prevalent (38.7%, 157 users), with 82.8% (130) perceiving a significant increase over five years. Initiation occurs primarily at 20+ years (81.5%, 128), but 15–19 years (18.5%, 29) is a critical youth risk period, reinforced by community perceptions (42.4%, 101).

Observation: The rising trend, driven by friends (58.0%, 217) and availability (37.8%, 94), is most pronounced among long-term residents (>10 years, p < 0.0001) and tertiary-educated individuals (p = 0.0009). Rare use dominates (62.4%, 98), but daily users (14.0%, 22) suggest potential dependency.

Objective 2: Major Causes of Weed Smoking

Key Finding: Curiosity (45.2%, 71), stress relief (31.8%, 50), and peer pressure (22.9%, 36) are primary reasons for use, with friends as the main influence (100%, 157 users; 87.1%, 217 community-wide) and source (58.0%, 91). Increased availability (37.8%, 94) is a key environmental driver.

Observation: High-risk groups include youth (15–19 years), lower-educated (SHS: 53.1%, primary: 45.7%), agricultural workers (logistic regression, p < 0.05), and long-term residents (p = 0.0000008). Causes vary by age (p = 0.0000002) and employment (p = 0.001), with local farms (26.1%, 41) and markets (15.9%, 25) facilitating access.

Objective 3: Negative Effects of Weed Smoking

Key Finding: Most users report no health (72%, 113), social (84.1%, 132), financial (84.1%, 132), or legal (100%, 157) impacts, but 28% (44) note reduced concentration and memory loss, and 15.9% (25) report negative social/financial effects. High/very high social stigma is perceived by 55.5% (136).

Observation: Limited negative effects suggest social acceptance or underreporting, with cognitive impairment as a key health corncern. Health impacts vary by gender (p = 0.04) and are highest among occasional users (78.4%, 29/37), though daily users report none (0%, 0/22).

Objective 4: Innovative Strategies to Reduce Prevalence

Key Finding: Awareness campaigns (37.1%, 149) and stricter law enforcement (25.4%, 102) are top strategies, with education/awareness (57.2%, 230) and peer-based methods (67.2%, 270) most supported. Families (30.3%, 122) and community leaders (16.9%, 68) are preferred leaders. Education (74.2%, 23) and limiting production (25.8%, 8) are key suggestions.

Observation: Despite strong support for community-driven strategies, 61.5% (247) rate interventions as ineffective, indicating skepticism that necessitates pilot programs. Peer mentorship (26.6%, 107) and economic empowerment (7.2%, 29) offer innovative, socially relevant solutions.

Overarching Insights Prevalence and Growth: Weed smoking is a significant issue (38.7% prevalence, 82.8% perceive increase), driven by social (friends, peer pressure) and environmental (availability, local sources) factors, particularly affecting youth and long-term residents.

Causes and Risks: Personal (curiosity, stress relief) and social (peer influence) motivations, amplified by easy access via friends, farms, and markets, create a high-risk environment for lower-educated and agricultural workers.

Limited Impacts: Minimal reported negative effects (health, social, financial, legal) contrast with high stigma (55.5%), suggesting social tolerance or underreporting, with cognitive impairment as a notable concern.

Community Solutions: Strong support for education, peer mentorship, and family-led interventions, combined with economic and supply-side strategies, reflects a community-driven approach, though low perceived effectiveness (61.5%) highlights the need for trust-building measures.

Demographic Variations: Age, education, employment, and residence duration shape trends, causes, and strategy preferences, necessitating targeted interventions for youth, lower-educated, and agricultural groups.

  • Implications for Policy and Practice

Targeted Interventions: Focus on youth (15–19 years) through peer mentorship and education, and support agricultural workers with economic empowerment to address stress and availability.

Community Engagement: Leverage families and community leaders to deliver culturally relevant awareness campaigns and mentorship programs, building on local trust.

Supply-Side Measures: Limit production and regulate local markets/farms to reduce availability, complementing demand-side education efforts.

Addressing Skepticism: Pilot innovative strategies (e.g., economic programs, production limits) and monitor outcomes to counter low perceived effectiveness and build community confidence.